add codes for testing

README.md

@@ -32,6 +32,33 @@ The fine-tuning was performed using the PEFT-LoRa technique, aiming to improve t
 - Generation of Yoruba text with correct diacritics
 - Natural language processing tasks for Yoruba language
 
+## Code for Testing:
+
+```python
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+config = PeftConfig.from_pretrained("Professor/yoruba-diacritics-quantized")
+model = AutoModelForSeq2SeqLM.from_pretrained("Davlan/mT5_base_yoruba_adr")
+model = PeftModel.from_pretrained(model, "Professor/yoruba-diacritics-quantized")
+tokenizer = AutoTokenizer.from_pretrained("Davlan/mT5_base_yoruba_adr")
+
+inputs = tokenizer(
+    "Mo ti so fun bobo yen sha, aaro la wa bayi",
+    return_tensors="pt",
+)
+
+device = "cpu" # use your GPU if you have
+
+model.to(device)
+
+with torch.no_grad():
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=10)
+    print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True))
+```
+
 ## Intended uses & limitations
 
 More information coming